Osa Conservation: Piro Restoration Experiment

Chris Beirne

1 Map of survey locations

This document summarizes the movement locations for all active devices within Osa Conservation’s movement ecology project.

The following represents the data exploration for the data collected during the collaboration between Osa Conservation and Earthwatch for the project entitled:

A multi-taxa approach comparing the success of active tropical rainforest restoration with old-growth forest in Costa Rica.

Also known as:

The Piro Restoration Experiment

1.0.1 Study area and survey locations

## Reading layer `trails_osa' from data source 
##   `/Users/runner/work/Piro_restoration_experiment_exploration/Piro_restoration_experiment_exploration/shapefiles/trails_osa.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 18 features and 2 fields
## Geometry type: MULTILINESTRING
## Dimension:     XY, XYZ
## Bounding box:  xmin: -83.35007 ymin: 8.393359 xmax: -83.30759 ymax: 8.427848
## z_range:       zmin: 0 zmax: 0
## Geodetic CRS:  WGS 84 + EGM96 height

## Reading layer `Restoration_experiment_original' from data source 
##   `/Users/runner/work/Piro_restoration_experiment_exploration/Piro_restoration_experiment_exploration/shapefiles/Restoration_experiment_original.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 40 features and 8 fields
## Geometry type: POLYGON
## Dimension:     XYZ
## Bounding box:  xmin: 570808.3 ymin: 928998.7 xmax: 571665.2 ymax: 930646.4
## z_range:       zmin: 0 zmax: 0
## Projected CRS: Transverse_Mercator

## Reading layer `roads_osa' from data source 
##   `/Users/runner/work/Piro_restoration_experiment_exploration/Piro_restoration_experiment_exploration/shapefiles/roads_osa.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 8 features and 1 field
## Geometry type: MULTILINESTRING
## Dimension:     XY
## Bounding box:  xmin: -83.46275 ymin: 8.389076 xmax: -83.27655 ymax: 8.533815
## Geodetic CRS:  WGS 84

# # First, set a single categorical variable of interest from station covariates for summary graphs. If you do not have an appropriate category use "project_id".
# category <- "feature_type"
# 
# # We first convert this category to a factor with discrete levels
# dep[,category] <- factor(dep[,category])
# # then use the turbo() function to assign each level a color
# col.cat <- turbo(length(levels(dep[,category])))
# # then we apply it to the dataframe
# dep$colours <- col.cat[dep[,category]]

m <- leaflet() %>%
  addProviderTiles(providers$Esri.WorldImagery, group="Satellite") %>%  
  addTiles(group="Base") %>%     # Include a basemap option too
  addPolylines(data = trails, group = "roads", color="brown") %>% 
  addPolygons(data=plots, popup=plots$plot_id, group="Plots") %>% 
  addCircleMarkers(lng=locs_db$longitude, lat=locs_db$latitude,
                   # Co lour the markers depending on the 'feature type'
                   #color=dep$colours,
                   # Add a popup of the placename and feature_type together
                   popup=paste(locs_db$plot_id, locs_db$original_treatment)) %>%
  ## Add a legend explaining what is going on
  # addLegend("topleft", colors = col.cat,  labels = levels(dep[,category]),
  #                  title = category,
  #                  labFormat = labelFormat(prefix = "$"),
  #                  opacity = 1) %>%
  # 
  # add a layer control box to toggle between the layers
  addLayersControl(
                    baseGroups = c("Satellite", "Base"))

m